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Heterogeneous uncoordinated deployment of WLANs: an evolving

problem for current and future Wi-Fi access

Murad Abusubaih*

ECE Department, College of Engineering, Palestine Polytechnic University, Hebron, Palestine

SUMMARY

This article addresses the problem of uncoordinated heterogeneous deployment of 802.11 wireless local areanetworks (WLANs). It is expected that such deployments by different WLAN owners and WiFi providerswill become a challenging problem that limits the network performance and quality of service of wirelessusers. We present results of a real case study that show a need for coordination among WLAN devices in

order to avoid current and future problems. We provide potential solution directions. A special focus is givento channel assignment and coordinated channel access problems. Our results show that a new paradigm fordesigning WLAN devices seems to be crucial. Copyright 2012 John Wiley & Sons, Ltd.

Received 3 December 2011; Revised 2 June 2012; Accepted 21 August 2012

1. INTRODUCTION

IEEE 802.11 wireless local area networks (WLANs) [1] today provide an infrastructure for wireless

Internet access to residential areas, businesses and public hotspots. The proliferation of wireless users

and the promise of converged voice, data and video technology is expected to open new numerous

opportunities for 802.11-based WLANs in the networking market.

Despite the current popularity of WLANs, their performance is rather far from being satisfactory. Huge

research activities have been launched in recent years in academia, industry and within standardization

bodies to solve the problems of current WLANs and enhance users quality of service (QoS).

Nonetheless, there is much room for improvement and many serious challenges still exist. WLAN

administrators try to improve users connectivity by deploying a high density of access points (APs).

However, the dense deployment of APs can introduce mutual interference, high collision rates and long

back-off intervals. This is due to the limited number of orthogonal channels the 802.11 standard supports,

which requires the assignment of the same channel to multiple APs that are close to each other. The

interference problem gets worse in residential areas and hotspots, where multiple WLANs are independently

deployed by different owners and WiFi access providers.

In 802.11 WLANs, interference may prohibit two nodes from sending packets, despite none of the

signals interfering with the receiver of the other. On the other hand, an interfering signal may damage a

packet being received, if its power is strong enough relative to the desired signal power at its receiver.Network planning strategies, usually carried out before network deployment, will not be good enough

to provide WLAN users with an acceptable QoS due to the dynamic nature and randomness of

network events, especially in residential environments where multiple WLANs owned by different

people are independently deployed.

In this article, we address the problem of uncoordinated deployment of WLANs. We present and

discuss results of a real case study that show a need for coordination among WLAN devices in order

*Correspondence to: Murad Abusubaih, Palestine Polytechnic University, Hebron, Palestine.E-mail: [email protected]

INTERNATIONAL JOURNAL OF NETWORK MANAGEMENTInt. J. Network Mgmt 2013; 23: 6679Published online 25 September 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/nem.1815

Copyright 2012 John Wiley & Sons, Ltd.

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to avoid current and future problems. We propose some solution directions for managing WLANs. In

this regards, we develop an architecture for cooperative WLANs management. Further, we develop

algorithms for channel assignment and coordinated channel access and apply them within the

proposed architecture. The rest of this article is organized as follows. In Section 2 we discuss recent

work related to WLAN management. In Section 3 the problem of uncoordinated deployments of

WLANs is discussed in detail. Section 4 provides insights on possible solutions. Section 5 presents

evaluation results before we conclude the article in Section 6.

2. BACKGROUND AND RELATED WORK

In the last few years several strategies for managing WLANs and improving their performance have been

proposed in the literature. Specifically, the paper of Panda and Kumar [2] develops a model for multi-cell

802.11 WLANs. Based ontheir model, the authors propose a scheme for channel assignment. Other

studies that address channel assignment in WLANs can be found [35]. The paper of Chieochan et al.

[6] provides a comprehensive survey on the state-of-the-art channel assignment schemes in 802.11

WLANs. Pandyaet al. [7] have developed a new strategy for back-off in 802.11 medium access control

(MAC). They utilize a frame loss differentiation algorithm. Also, Tinnirello and Sgora [8] propose a

scheme for differentiating frame losses in 802.11 networks. In their extended work [9] the authors

propose a new back-off decrement model for 802.11 MAC. The model has shown good potential

compared to other schemes. Yen et al. [10] summarizes the recent studies proposed to solve the problem

of load balancing in 802.11 WLANs. The results have shown that load balancing can increase system

throughput. Coordinated transmission has been addressed in several papers [1113]. To cope with

increasing throughput requirements, 802.11n has been developed. The performance of the system is

analyzed in Pollin and Bahai [14]. The authors follow an approach similar to Bianchi models for legacy

802.11 and focus on the double channel bandwidth provided by the 802.11n standard.

In almost all cited efforts, attention was given to a network that belongs to one administrative

domain, which is not the case as WLANs continuously evolve. In this work, more focus is given to

heterogeneous deployments of WLANs by different administrative domains, where the management

of such networks is a challenging and crucial issue. We develop a system architecture for facilitating

the management of heterogeneous WLANs and apply algorithms developed for channel assignment

and coordinated access within this architecture.

3. HETEROGENEOUS AND UNCOORDINATED DEPLOYMENT OF WLANS

When the WLAN design was first developed in 1990, the model assumed that a WLAN deployment

comprises one stand-alone AP. In fact, such a system provides a satisfactory user experience as long as

there are few users with relatively light traffic load and one AP. Owing to the rapid increase of wireless

users and the requirement for continuous coverage, nowadays multi-AP WLANs span buildings or

floors. Some neighboring APs are configured on the same channel because of the limited number of

channels the 802.11 standard supports.

A consequence of dense WLAN deployments coupled with the limited number of channels is that

interference is the main serious and challenging problem and methods to mitigate it are essential.Interference increases collisions, contention and back-off intervals. Inevitably, the capacity of the

WLAN and the performance that wireless users experience precipitously drop due to interference.

The problem becomes obvious and even worse when WLANs owned by different administrative

bodies coexist in the same area like hotel series, dense streets and apartments. Each WLAN owner

simply buys and deploys one or multiple APs without caring about the basic configuration and

deployment guidelines.

Although new designs for future IEEE 802.11 WLANs are being developed, the focus is being

given to the enhancement of the physical and medium access control layers. Developers of the

802.11n try to achieve a high data rate of 100s Mbps at the physical layer by adopting multi-input

multi-output orthogonal frequency division multiplexing (MIMO-OFDM) technology. MAC

67HETEROGENEOUS UNCOORDINATED DEPLOYMENT OF WLANS

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enhancements through aggregated acknowledgments improve the MAC throughput. However, such

design enhancements could be useful within a stand-alone WLAN. When multiple providers or owners

independently deploy their networks, the performance aimed becomes questionable.

We believe that more effort and attention have to be given to the optimization of multiple heterogeneous

WLAN deployments. Specifically, the following issues must be addressed:

the possibility of optimizing channel settings of WLAN APs owned by different service providers or

administrative domains;

the possibility of coordinating the operation of WLANs owned by different service providers or

administrative domains;

the possible collaboration among different WiFi access providers for the objective of power

consumption reduction and better QoS levels;

the coexistence of different WiFi technologies.

Solutions to the previous issues are crucial for future WLAN designs. In the following paragraphs,

we extend the discussion of the previous open issues.

3.1. Channel assignment

Channel assignment in 802.11 WLANs has been extensively addressed in research papers. But, in

almost all considered models, it is assumed that all WLAN nodes belong to one network and the

optimization is performed within this network. Further, implementation aspects are rarely discussed.

Although a solution within each network might be optimal, collaboration among different neighboring

WLANs for a global optimal solution is necessary. Some APs are currently provided with a dynamic

channel selection algorithm. With such algorithm, an AP tries to select the least interfered channel.

However, in real deployments, such APs will continuously hop or oscillate over the channels as long

as they operate independently. Thus efficient solutions should consider the development of algorithms

and protocols for collaborative setting of WLAN channels that even belong to different bodies.

3.2. Coordinated WLAN operation

Even with efficient channel assignment strategies, the network performance and QoS in dense areas

might not be acceptable. This is expected during high load periods, during which interference becomeshigh. Numerous proposals for coordinated channel access of WLAN nodes have emerged in recent

years. Again, such proposals are limited to scenarios of a single WLAN owned by a single operator

or service provider. Extensions for development of collaborative protocols and policies among different

independent WLANs are still necessary.

3.3. Reduction of power consumption

A new paradigm is evolving within cellular networks, referred to as green communication. The idea is

to switch off some cellular base stations (BSs) when it is possible to provide an acceptable service to

active customers without these BSs being on. This leads to a reduction in the overall consumed power.

The same idea was considered for WiFi devices but within the same network. It would be useful to

introduce collaboration among different providers to provide WiFi access with fewer active APs. Thiswould be possible especially during low traffic periods.

3.4. Coexistence of different WLAN technologies

802.11 devices based on different IEEE standards are seen to coexist in different places especially across

residential areas. Although the standard requires backward compatibility of emerging technologies, it

does not normally address the effect of using heterogeneous devices in the same area. It is known that

802.11b transmits maximally at 11 Mbps using the direct sequence spread spectrum (DSSS), while

802.11 g devices can modulate at a maximum speed of 54 Mbps. Further, the new 802.11n products

use different physical and MAC layer designs and can transmit at 100s of Mbps. Assuming average

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per user frame size, the time required to deliver frames in each technology is different. We have studied the

mutual effect among the different technologies [15]. The results have shown that backward compatibility

comes at a high cost.

In the next sections, special focus is given to the development of solutions for channel assignment

and coordinated channel access problems in residential areas.

4. INSIGHTS INTO POSSIBLE SOLUTIONS

From the previous discussion, it is obvious that the management and configuration of heterogeneous

WLANs should be performed in a joint manner. Owing to the dynamic nature of the wireless environment

and users behavior, we believe that the best way towards efficient WLANs management is to

follow measurement-based optimization, where the inputs to optimization algorithms are taken from

measurement of parameters such as interference, collision rate and load. The challenging aspects are:

The optimizer: where it should be located? A first option would be to use one of the APs as a

management center. With this option, APs communicate and exchange measurements over the

air or the wired backhaul. For this option, users stations canbe also used for over-the-air report

exchange. The new 802.11z standard, which enables stations to communicate without going

through APs, is expected to be very helpful for achieving this goal. However, this option is solelypossible if wireless links can be found to the management AP.

In this paper, we propose an architecture for managing APs of different WLANs. The architecture is

based on a dedicated server owned by one of the service providers. APs report measurements to the

server which will in turn run optimization algorithms and determine the appropriate configuration

for each participating AP. Figure 1 illustrates this architecture. Obviously, a protocol is needed for

the exchange of measurements among APs and the management server. Special messages to convey

commands and measurements need to be defined. This protocol is left for our future work.

1. The mechanisms required to report measurements by stations to APs. IEEE 802.11 k standard

provides a set of useful measurement reports with special frame structures. They can be used

within the proposed architecture.

2. Interoperability among devices from different vendors. This requires the need to refi

ne standardsso as to address and facilitate the possible collaboration and joint optimization of WLAN

configuration. Joint optimization means here the optimization of operational parameters of

networks that belong to different administrative domains.

Figure 1. System architecture for managing WLANs



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The rest of this section details algorithms for channel assignment and coordinated channel access in

real and dense WLANs. The algorithms will run on the optimization server.

4.1. System model

We consider a dense WLAN deployment forNproviders (WLAN administrative domains). All providers

participate in the joint network optimization. The network of providern has Nn APs. We further assumethat APi uses channel Ci and transmit power level Pi. APs report measurements about neighboring APs,

interference and load to the management server. We assume that APs are able to communicate with this

server through IP connections. The task of the server is to find out the optimal configuration of each

participating AP which leads to better performance within each provider network. A general mathematical

formulation of the problem is as follows. Find the configuration that

maxXNn1

xn

s: t: xn > xnb

where

xn X

APi2NnU APi

(1)

where U(APi) is a utility function for each BSS served by APi. xn is the utility of providers n network.

Although, in this article, the throughput is used as the utility measure, the utility can be a combined value

of different weighted measures. xnb is the utility measure for providers n network with the current

configuration (i.e. without or before joint optimization).

4.2. Channel assignment

In this subsection, we specifically formulate the problem for channel assignment. A common

shortcoming of many algorithms proposed for channel assignment in the literature is that they do

not completely discuss and specify the implementation procedure. Further, different algorithms use

the distance between wireless APs as an interference measure. They configure APs which are closeto each other on different channels. In addition to the fact that accurate distance measurement for

indoor WiFi deployments do not exist yet, interference conditions and the signal strength depend on

many other parameters.

Measurement-based approaches also face challenges regarding their implementation. The challenging

problem here is that a measuring node only observes transmissions sent over the same channel it uses.

i.e. it does not see transmissions over other channels. Therefore, the knowledge of signal strength

among all node pairs requires a special mechanism.

In this article, we develop a measurement-based approach. In this approach, wireless nodes (stations

and their APs) measure signal activities from each sending address. In order for a node to perform

measurements over channels other than the one it operates on, it switches the operating channel and

starts measurements. However, the switching and measurement take place during long back-off periods

(which are expected during high-interference conditions). Further, measurements over the same channelare distributed over non-contiguous small time intervals. We refer to this measurement protocol as

non-disruptive measurement, i.e. measure while operate. The main feature of this protocol is that it

does not require a node to perform measurements over all channels consecutively. Thus it gradually

builds up a measurement report. For real implementation, 802.11 k reports can be used for the delivery of

measurements to the APs. In order to avoid simultaneous measurements by nodes, the management server

successively instructs each AP to provide measurement results. Through 802.11k reports, an AP instructs

each associated node to perform and report measurements.

Suppose that we have R nodes (stations and APs). Suppose that Pkm represents the power level

measured at node k from node m and akm is the activity level of node m measured at node k. The

activity level parameter is introduced here to model the fact that interference from a node is only present

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when a node transmits and not always. Thus measuring high signal strength from a transmitter would only

be harmful if the transmitter is highly active. Therefore, an interfering node is not considered harmful if it

rarely transmits. A node kestimates the activity level of node m as follows:

akm 1

T

XGi1

Lmi

Rmi(2)

where Lmi denote the length in bits of frame i received from interferer m, respectively. Rmi denotes the

physical rate in bits/second at which frame i is received from interferer m, G denotes the number of

received frames from interfering node m during the measurement period and T denotes the length of

the measurement period.

For a certain channel configuration, the total interference level expected at node kfromR interfering

nodes can be computed as

Ik XRm1

PkmakmOkm (3)

where Okm is the overlapping channel interference factor, defined as

Okm 1 Cm Ckj j=5 Okm 0

0 otherwise

(4)

where Cm is the channel used by node m and 5 is the number of channels between two consecutive

non-overlapping channels (1, 6, 11). Note that if channel 1 is used by node m and node k, then

Okm=1 or 100%. If channels 1 and 5 are used by nodes m and k, then Okm = 0.2 or 20%. If channel

1 is used by node m and channel 6 or higher is used by node k, then Okm=0 or 0%.

The throughput Thkof node kis estimated by its experienced signal-to-interference ratio (SIR), given as

Thk Blog2 1 Pk

Ik

(5)

where B is the channel bandwidth and Pk is the power level measured at node k from its AP. The total

throughput within the network of providern is thus given as

Thn Xi2Z

Thi (6)

where Zis the set of nodes that belong to provider ns network.

The optimal channel assignment is one that maximizes

maxXNn1

Thn

s:t: Thn > Thnb

(7)

where Thnb is the total throughput measured across the entire network of provider n before

confi

guration. In our simulation experiments, this optimization problem has been solved usingthe LINGO optimization package [16].

4.3. Analysis of signaling overhead

In this subsection we analyze analytically the signaling overhead of the joint channel assignment

scheme in terms of delay due to message delivery by APs to the management server.

APs are connected via full duplex IEEE 802.3 Ethernet switches and routers. The performance

metric of interest is the time delay caused by the exchange of coordination messages between a cluster

of neighboring APs and the management server. Note that the frequency of coordination depends on

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To analyze the delay, denote the message length as Lbytes, the frame error probability due to frame

errors as Pe, the probability of error due to collisions as Pc, and the line speed between each AP and the

management server as B Mbps. We start with the formula for the average time required to transmit a

frame (message) of length L successfully:

Tsuc Tf Pfail

1 Pfail

Tfail (8)

where Tf is the time of a frame transmission. Pfail and Tfail are the probability of a failed transmission

and its time span respectively. Tf depends on the line speed to reach the management server, the mes-

sage length and the buffering time insider wired network elements. It can be written as

Tf 8L=B Tbuff (9)

where Tbuff is the latency in the buffers of network devices which could reach in the worst case 10 3

second or even less in todays switching technologies. In fact, the transmission can fail due to frame

errors or collisions which are independent events. Therefore:

Pfail PePcPePc (10)

Tfail is given as

Tfail Tf Twait (11)

where Twait captures retransmissions timers time-out and back-off time in case of collisions. Putting

things together, Tsuc can be written as

Tsuc 8LBPfail Twait Tbuff

B 1 Pfail Tbuff (12)

Thus the total delay due to transmitting and receiving Mmessages for the purpose of measurement

reporting by a sending AP to the management server is D =MTsuc.

4.4. Coordinated channel access

After channel assignment and if the network performance is still low due to interference, groups of APsthat run over the same channel coordinate their transmissions using a time-slotted scheme. We have

shown the potential improvement of operating highly interfering WLAN cells in a coordinated manner

in Abusubaih et al. [17]. The recent paper of Leonovich and Ferng [11] recommends a similar approach

and concludes similar results. We developed a scheduling algorithm published in Abusubaih et al. [17].

One key challenge for applying the coordination-based channel access approach in residential areas

is the grouping of APs that use the same channel. In this article we develop a density-based clustering

technique to solve this problem. Density-based clustering techniques were originally developed to

recognize dense areas (e.g. objects in images) within an object space. They have the advantage of

allowing arbitrary shape of clusters and do not require the number of clusters as input. The most

common approach for density-based clustering is so-called bump-hunting. This starts finding dense

spots or hotspots and then expands the cluster boundaries outward until it meets a low-density region.

We apply this approach for clustering interfering regions. Specifi

cally, the goal of the clusteringalgorithm here is to determine the groups or clusters of interfering BSSs.

We model our WLAN as a weighted conflict graph G = (V;E;W), where V= {1;2;3; . . .;K} is the set

of vertices or nodes which represent the BSSs of the WLAN, Kis the total number of nodes and Eis the

set of edges between the nodes. The weight wij on an edge connecting two nodes i and j denotes the

amount of interference estimated within node jfrom node i. From the conflict graph, the set of loosely

coupled or independent clusters of nodes need to be identified. An informal algorithm for solving

this problem is as follows. Each cluster starts at the cluster root (a BSS that measures the highest

interference in the cluster) and expands until the interference between any cluster member and its

neighbors at the cluster borders is less than a threshold value. Thus the algorithm starts with the BSS

that experiences the highest interference from its neighbors, say BSS I. This will be afirst cluster root.

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The algorithmfinds out all interfering neighbors of BSS I. All these BSSs will be members of the first

cluster. Then, the algorithmfinds out the neighbors that interfere with each member of thefirst cluster. It

expands on a path until the interference impact of a BSS and its neighbors is below InterfThreshold.

Then the next cluster root is selected. Similarly, all interfering BSSs and their neighbors are included

in the new cluster until a light region is detected. The algorithm continues until each BSS is a member

of a cluster. The formal algorithm is given in Algorithm 1.

Algorithm 1 Clustering algorithm

1: INPUT: An interference map of the WLAN BSSs.

2: OUTPUT: Members of each cluster.

3: Mark all BSSs as Non-Member.

4: while (there is some BSSs that are still Non-Member)

5: {

6: Next = The Next BSS I that experiences highest interference;

7: Construct a new Cluster;

8: ADDInterferers(Next);

9: }

10: Output Clusters;

11: ****************************************************

12: ADDInterferers(I) {

13: If (no more interferer BSS to I)

14: return();

15: J = Select the next Interferer to BSS I;

16: Include J as a member of cluster I;

17: Mark J as DONE ;

18: ADDInterferers(I) ;

19: ADDInterferers(J) ;

20: }

5. PERFORMANCE EVALUATION

In this section we assess the ability of the joint optimization strategy to improve the performance of a

high-density real WLAN. We focus on real case scenarios and conducted detailed simulation experiments

using realistic WLAN traces. The NCTUns simulation package [18] has been used. In NCTUNs, the

802.11 modules are ported from the NS2 simulator. The MAC layer goodput is the metric to be observed.

Every user and AP measures it during a time interval of 1 second. The channel and slot assignment

algorithms and interference estimation algorithms are fully implemented, while the signaling protocol

for the exchange of information among users and their respective APs is not. We simply make the

measurement information accessible to APs.

5.1. Signaling overhead

We start by plotting the delay experienced by a sending AP to the management server for the purpose of

collaborative settings. Figure 2 plots the maximum delay versus packet error probability for different

numbers of delivery messages. The results show that a central management of large groups causes longer

delays, especially at high error probabilities. However, at moderate error rates and if an AP does not report

all measurements sequentially, the delay is reasonable. However, this comes at the cost of delaying the

relieving of the network from high interference. Owing to technology advancements, it is expected that

the delay will even be lower than that of Figure 2.



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5.2. WLANs in residential areas

The discussion in the previous section motivates us to study a WLAN in a rather dense unplanned

deployment. This will provide:

knowledge about how deployed WLANs may look like in reality;

a sharpened understanding of side effects and any potential issue that will be considered further.

A residential area which is known to be quite dense is the city of San Diego (USA), where hundreds or

even thousands of APs are being deployed by individuals and Internet service providers. We used data

from the WiMaps.com website. The data are obtained through war-driving. For each AP, the database

provides the APs geographic coordinates, its wireless network ID (ESSID), channel employed and the

MAC address. We have used a Geographical Information System (GIS) visualization tool to plot the

points (APs) using their geographic coordinates.

5.2.1. Observations

Figure 3 shows all APs that operate over the three non-overlapping channels 1, 6 and 11. From thisfigure, we observe the following:

Most of the APs (found to be about 1150) in the shown region operate on channel 6. This is the

default channel configured by APs manufacturers, leading to the result that people seem to

deploy the APs without any planning. Additionally, more APs are configured on channel 11

(about 300) than channel 1 (about 160).

Figure 3. A residential area in San Diego: APs configured on channels 1, 6 and 11

Figure 2. Delay of message delivery by an AP to the management server

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There seem to be dense regions and some light regions, which advocates the necessity of grouping

BSSs into clusters for the sake of coordination.

Few APs are found on channels other than 1, 6 and 11.

5.3. Clustering results

We applied the clustering algorithm provided in Algorithm 1, grouping interfering BSSs into clusters. A

fundamental question is the selection of the interference threshold InterfThreshold, the threshold above

which two BSSs will be identified as interfering neighbors, requiring an edge between them in the conflict

graph. Owing to the lack of any other interference-relevant information in the database, we decided to use

the only real information available: the location of APs. A script has been used for computing the distance

between APs from their GPS coordinates. We assumed that two BSSs are interfering if the distance

between them is less than 250 m. Additionally, the mostly interfered BSS is assumed to be the one that

has more in-range APs on the same channel. The results of this test are provided in Table 1.

We make the following comments on these results:

Despite the high density of APs, it is likely possible to group them into loosely coupled or

independent clusters.

From about 1610 APs in the selected region, we have found that only 144 (9%) of them are

isolated, i.e. they have no neighbors (one AP cluster).

Although 25 clusters on channel 6 have more than 10 APs, only a few clusters on channels 1 and

11 have more than 10 APs. This is expected since channel 6 is used by 72% of the APs.

Based on the current channel settings in the considered area, coordinated Channel access seems to

be more feasible and flexible within the clusters, wherein APs operate on channels 1 and 11. This is

because of the small number of APs within these clusters. Nonetheless, 38 clusters that operate on

channel 6 are found to have between two and nine members. Note also that not all members within a

cluster may interfere with each other (i.e. the BSSs graph may not be fully connected).

5.4. Simulation setup

In the simulation experiments, APs are connected to a server (via an 802.3 switch) through cables of

100 Mbps bandwidth. The latency for packets between APs and the server was set to 10 ms. All nodesimplement the 802.11b technology. Depending on the distance between AP and user, the wireless channel

is attenuated more or less severely. However, we assume that radio signals are not only attenuated by path

loss, but are also affected by fading due to multipath propagation. In order to accurately model these

effects, a path loss component as well as a Rayleigh-distributed fading component are considered. For

the path loss, a two-ray ground reflection model has been used with the received power Prx given as

Prx PtxGtxGrxhtxhrx

d4(13)

where Ptx is the transmit power (in mW), Gtx,Grx denote the transmitter and receiver antenna gains

respectively, htx and hrx are the antenna heights of transmitter and receiver and dis the distance between

them. A Rayleigh fading model provided by the NCTUns simulator is used. It takes as parameters thereceived power Prx and a fading variance set to its default value of 10 dB. The received power level of

a packet (with respect to both path loss and fading attenuations) is computed at the beginning of the packet

and assumed to be constant over the whole packet length. It is passed to an error module along with packet

Table 1. Clustering results

Channel No. of clusters with APs 10 No. of one AP clusters No. of clusters with APs> 10

1 86 52 26 102 38 2511 117 54 3



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length and modulation type. This module determines whether a received packet is correct or corrupted

due to fading and path loss attenuation.

The aggregated combined power level (the RCPI in 802.11 k) of two packets if one arrives while the

other is being received is computed at the receiver as follows:

Ptotal

PfTf PnToverlapTf (14)

where Pf is the received power level of the first packet, Tf is the duration time of the first packet, Pn is the

received power level of the new incoming packet and Toverlap is the time it overlaps with the first packet.

Wireless nodes choose their transmission rates depending on the perceived, average SNR and try to

assure a bit error rate (BER) less than 10 5. This rate remains constant during the simulation, i.e. no rate

adaptation mechanism has been implemented. Packet capturing is modeled in the simulator as follows.

While simulating packet reception time (a function of physical rate, packetsize), if a new packet arrives

and the power level of the first packet is greater than the power level of the new packet by at least the

capture threshold, then the first packet is assumed to be received and the new packet is ignored. Constant

simulation parameters are provided in Table 2.

5.4.1. Controlled scenario

In this scenario, 50 APs are deployed in an area. Their channels are randomly assigned. The distance

between APs was fixed to 150 m. Users are randomly deployed across the coverage area of APs. The

number of users is varied. Users send 1500bytes packets to their APs and each one uses a different

time interval between its successive packets. Other simulation settings are similar to those used in

the next subsection.

5.4.2. Real case scenario

In this scenario, we consider a set of 100 APs from the residential dense area of the San Diego coast.

500 stationary users are randomly distributed across the coverage area of all APs.

Since the interference depends on users workload, in this scenario we rely on realistic WLAN traffic

traces provided in Ergen [19]. We used the realistic WLAN traces in the following way. Using CoralReefSoftware [20], we extracted users flows from the dump file. We selected users flows over 10 minutes,

within which a high load interval was found. We used the total number of bytes and number of packets

of aflow to characterize a user load and compute an average packet length. Then, the stg (synthesized

traffic generator) provided by the NCTUns tool was used to emulate users flows.

Table 2. Constant parameters

Parameter Value

PLCP header TH 48msPLCP preamble TP 144 msRec. power threshold -100 dBm APs/users Tx power 100 mW

RXThreshold (1 Mbps) -94 dBm RXThreshold (11 Mbps) -82 dBm RXThreshold (5.5 Mbps) -87 dBm RXThreshold (2 Mbps) -91 dBm TSIFS 10msTDIFS 50msTSlot 20msTCWmin 31TCWmax 1023Gtx , Grx 0 dBihtx and hrx 1 mFading variance 10 dB

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5.5. Results of channel assignment (controlled scenario)

We compare different algorithms for channel assignment. Results are shown in Figure 4. The first

algorithm is simply based on distance, wherein APs that operate over the same channel are kept as far

as possible. We refer to it as Algorithm 1. Also, we implemented our proposed measurement-based

algorithm without (akm=1) and with activity level consideration (Algorithms 2 and 3 in the figure).

Further, we were able to implement the algorithm of Broustis et al. [3]. This algorithm minimizes the

amount of power sensed at each AP from its co-channel APs. We refer to it as Algorithm 4 in Figure 4.

Generally, a low goodput can be observed as the number of users increases. The distance-based channel

selection (Algorithm 1) has marginally improved the goodput performance. Measurement-based channel

assignment that considers users activity level (Algorithm 3) achieves better performance than one that

assumes the same user load (Algorithm 2), especially under high interference conditions. When the number

of users is 250, the gain in goodput is more than 100%. The algorithm proposed in Broustis et al. [3] has

been found to achieve comparable performance to our proposed measurement-based algorithm without

consideration of activity level. These results reinforce the need to use measurement-based algorithms.

5.6. Results of channel assignment and coordinated channel access

Figure 5 shows the results of simulation experiments. The figure plots the network goodput withstandard 802.11b protocols (with current channel settings, i.e. without implementing the proposed

Figure 4. Comparison of channel assignment algorithms

Figure 5. Network goodput with joint optimization



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algorithms for channel assignment and coordinated access), and the goodput with channel assignment

and coordinated channel access.

The results show that disjoint channel assignment, wherein individual APs select their channels

independently, has improved the aggregate goodput compared to legacy 802.11. Nonetheless, joint

channel assignment (within each cluster) outperforms the disjoint channel assignment. The results also

show that channel access coordination has a significant positive impact on the aggregate goodput

during high interference periods (between 150 and 350 seconds). Spikes seen in the aggregate throughputare due to bursty traffic and unsaturated channel conditions.

6. CONCLUSIONS

This paper addresses the problem of uncoordinated deployment of WLANs. A study of realistic

WLANs in residential areas shows that future WLANs will face real challenges unless designers

and administrators pay attention and adopt solutions for the coordination of WLANs operation. We

pointed out some potential solution directions. A special focus is given to development of algorithms

for joint channel assignment and coordinated access. Currently, we are developing signaling protocols

for the implementation of the proposed approaches.

REFERENCES

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WoWMoM 2009, 2009.

18. NCTUns Simulator. Available: http://nsl.csie.nctu.edu.tw/nctuns.html [12 September 2012].

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AUTHORS BIOGRAPHY

Murad Abusubaih received the B.Sc. degree (with honors) in computer systems engineering from Palestine

Polytechnic University, Hebron, Palestine, in 1998, the M.Sc. degree in communications engineering from

Jordan University of Science and Technology, Irbid, Jordan, in 2001, and the P.hD. in Communications Engineer-

ing from Technical University Berlin, Berlin, Germany, in 2009. From 2001 to 2004 he worked as lecturer at

Palestine Polytechnic University. From 2005 to 2009, he worked as Research Assistant at Technical University

Berlin. Currently, he is an Assistant Professor at Palestine Polytechnic University.



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